With the advent of electronic or E-commerce, customer-to-customer (C2C) online shopping has become and continues to grow as a popular business model. In typical E-commerce platforms, business transactions may be conducted directly between individual vendors and customers. E-commerce C2C provides a free and wide-open market for people to find the products and services they desire. Downsides include the problem of finding appropriate products and services, and distinguishing authentic from fake goods. Ideally, product and vendor should be considered together during transactions.
Product or item retrieval is a core part of online shopping platforms. A product or item retrieval system presents appropriate products from an overwhelming number of available products to users/customers. A typical product or item retrieval system may include an index of items created by an index operator. The most relevant items corresponding to customer's query is retrieved. Results of the retrieved items may be arranged by a ranking model according to the scores from a ranking function.
However, such conventional retrieval systems are not efficient or effective. For example, conventional item retrieval systems do not take into consideration the personality of users. In other words, for a given query, an item retrieval system returns the same result for every customer. For example, two customers, customer 1 and customer 2, desire to buy “t-shirts” from the online shopping platform. Customer 1 prefers brand name “t-shirts” with a higher quality. Customer 2 prefers “t-shirts” with a lower price. The typical item retrieval system would return the same layout of a website based on facts such as release date and turnover. Because of this, “t-shirts” with larger sale volumes or new releases are ranked at the top of the retrieval result. The ranked result of items that are distributed in the entire price range may not suitable for both customer 1 and customer 2 in this case. This may result in one customer spending longer time before finding the desired item to purchase.
Furthermore, conventional retrieval systems may not have insight as to a customer's search motivation from key words of a searching input, which is useful in the awareness of what a retrieval order should be. For example, a user may input key words of “basketball shoes” and another may input “AIR JORDAN”. This imply different requirement from the customer's viewpoints. Searching for “basketball shoes” usually indicates that the customer desires to buy basketball shoes, without context as to the customer's preference, such as brand or price range. As such, a comprehensive listing, including the most popular items should be helpful. In contrast, the user who wants to find an “AIR JORDAN” knows quite well about what he or she wants. For example, the user has an expectation of high quality and at a price that commensurate with such quality. In such case, the system should provide specific items from various dimension (e.g., cost performance). Branded shoppers and cost conscious or bargain shoppers may have different objectives. Customizing a search result allows consideration of a shopper's preference, thus making online shopping more efficient and pleasurable for customers.
From the foregoing discussion, it is desirable to provide an effective and efficient online shopping platform.